Zimi Sawacha1, Cristina D Sartor2, Liu Chiao Yi3, Annamaria Guiotto4, Fabiola Spolaor5, Isabel C N Sacco6. 1. Department of Information Engineering, University of Padova, Padova, Italy; Department of Medicine, DIMED, University of Padova, Padova, Italy. Electronic address: zimi.sawacha@dei.unipd.it. 2. Ibirapuera University, São Paulo, Brazil; Physical Therapy, Speech and Occupational Therapy Department, School of Medicine, University of Sᾶo Paulo, São Paulo, Brazil. Electronic address: crisartor@gmail.com. 3. Federal University of Sao Paulo, Santos, São Paulo, Brazil. Electronic address: liuchiaoyi@hotmail.com. 4. Department of Information Engineering, University of Padova, Padova, Italy. Electronic address: guiotto@dei.unipd.it. 5. Department of Information Engineering, University of Padova, Padova, Italy. Electronic address: fabiola.spolaor@unipd.it. 6. Physical Therapy, Speech and Occupational Therapy Department, School of Medicine, University of Sᾶo Paulo, São Paulo, Brazil. Electronic address: icnsacco@usp.br.
Abstract
BACKGROUND: It is well recognized that diabetes and peripheral neuropathy have a detrimental effect on gait. However, there are large variations in the results of studies addressing this aspect due to the heterogeneity of diabetic population in relation to presence and severity of diabetes complications. The aim of this study is to adopt an unsupervised classification technique to better elucidate the gait changes throughout the entire spectrum of diabetes and neuropathy. METHODS: Sixty subjects were assessed and classified into four groups using a fuzzy logic model: 13 controls (55 ± 7years), 18 diabetics subjects without neuropathy (59 ± 6 years, 11 ± 7 diabetes years), 7 with mild neuropathy (56 ± 4years, 19 ± 7 diabetes years), and 22 with moderate to severe neuropathy (57 ± 5 years, 14 ± 8 diabetes years). Data were gathered by six infrared cameras at 100 Hz regarding lower limb joint kinematics (angles and angular velocities) and the relative phase for the hip-ankle, hip-knee, and knee-ankle were calculated. The K-means clustering algorithm was adopted to classify subjects considering the whole kinematics time series. A one-way ANOVA test was used to compare both clinical and kinematics parameters across clusters. RESULTS: Only the classification based on the intralimb coordination variables succeeded in defining 5 well separated clusters with the following clinical characteristics: controls were grouped mainly in Cluster 2, diabetics in Cluster 4, and neuropathic subjects in Cluster 5 (which included various degrees of severity). Hip-ankle coordination in Clusters 4 and 5 were significantly different (p < 0.05) with respect to Cluster 2, mainly in the stance phase. During the swing phase, differences were observed in the ankle-knee coordination (p < 0.05) across clusters. CONCLUSION: Classification based on intralimb coordination patterns succeeded in efficiently categorize gait alterations in diabetic subjects. It can be speculated that variables extracted from sagittal plane kinematics might be adopted as a support to clinical decision making in diabetes.
BACKGROUND: It is well recognized that diabetes and peripheral neuropathy have a detrimental effect on gait. However, there are large variations in the results of studies addressing this aspect due to the heterogeneity of diabetic population in relation to presence and severity of diabetes complications. The aim of this study is to adopt an unsupervised classification technique to better elucidate the gait changes throughout the entire spectrum of diabetes and neuropathy. METHODS: Sixty subjects were assessed and classified into four groups using a fuzzy logic model: 13 controls (55 ± 7years), 18 diabetics subjects without neuropathy (59 ± 6 years, 11 ± 7 diabetes years), 7 with mild neuropathy (56 ± 4years, 19 ± 7 diabetes years), and 22 with moderate to severe neuropathy (57 ± 5 years, 14 ± 8 diabetes years). Data were gathered by six infrared cameras at 100 Hz regarding lower limb joint kinematics (angles and angular velocities) and the relative phase for the hip-ankle, hip-knee, and knee-ankle were calculated. The K-means clustering algorithm was adopted to classify subjects considering the whole kinematics time series. A one-way ANOVA test was used to compare both clinical and kinematics parameters across clusters. RESULTS: Only the classification based on the intralimb coordination variables succeeded in defining 5 well separated clusters with the following clinical characteristics: controls were grouped mainly in Cluster 2, diabetics in Cluster 4, and neuropathic subjects in Cluster 5 (which included various degrees of severity). Hip-ankle coordination in Clusters 4 and 5 were significantly different (p < 0.05) with respect to Cluster 2, mainly in the stance phase. During the swing phase, differences were observed in the ankle-knee coordination (p < 0.05) across clusters. CONCLUSION: Classification based on intralimb coordination patterns succeeded in efficiently categorize gait alterations in diabetic subjects. It can be speculated that variables extracted from sagittal plane kinematics might be adopted as a support to clinical decision making in diabetes.